Methods, Systems and Appratuses for Optimizing the Bin Selection of a Network Scheduling and Configuration Tool (nst) by Bin Allocation, Demand Prediction and Machine Learning

2. The method of claim 1 wherein the optimum bin selection using a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) application to adjust a time slot selection algorithm policy for each bin.

7. The method of claim 6, wherein the goal is constrained by a set of factors including the lack of available bins, the packing efficiency of the bins, and the remaining time in the bins after scheduling.

13. The system of claim 12, wherein the optimum bin selection implements a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) application to adjust a time slot selection algorithm policy for each bin.

18. The system of claim 17, wherein the goal is constrained by a set of factors including the lack of available bins, the packing efficiency of the bins of the collection, and the remaining time in the bins after scheduling.

20. The apparatus of claim 19, wherein the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) application adjusts a time slot selection algorithm policy for each bin for optimum bin selection, and treats, via the MADDPG application, each bin as an independent agent to perform calculations of demand values at a beginning of a scheduling cycle over a duplicate set of global bins which represent an entire schedule cycle using a bin demand property on each bin of a global bin set.